DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm

• Published in: Applied soft computing Vol. 129; p. 109622
• Main Authors: Aboud, Ahlem, Rokbani, Nizar, Fdhila, Raja, Qahtani, Abdulrahman M., Almutiry, Omar, Dhahri, Habib, Hussain, Amir, Alimi, Adel M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022
• Subjects: Detectors; Dynamic multi-objective optimization problem; Dynamic particle swarm optimization; Dynamic response; Friedman analysis of variance; Lyapunov theorem; Wilcoxon test; Dynamic response; Wilcoxon test; Dynamic multi-objective optimization problem; Dynamic particle swarm optimization; Lyapunov theorem; Detectors; Friedman analysis of variance
• ISSN: 1568-4946; 1872-9681; 1872-9681
• DOI: 10.1016/j.asoc.2022.109622

Particle Swarm Optimization (PSO) system based on the distributed architecture over multiple sub-swarms is very efficient for static multi-objective optimization but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes in the objective function due to time-varying parameters. A response strategy consisting in reevaluating all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), hypervolume difference (HVD), Robust IGD (RIGD), and Robust General Distance (RGD) metrics are used to assess the DPb-MOPSO performance. Quantitative results are analyzed using Friedman’s analysis of variance, and the Wilcoxon sum ranks test, while the stability is analyzed using Lyapunov’s theorem. The DPb-MOPSO is more robust than several dynamic multi-objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 8/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO can resolve 7/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 6/8 with moderate changes. DPb-MOPSO assumes 7/8, 6/8, and 5/8 for solving FDA, and dMOP functions on IGD and 6/8, 5/8, and 5/8 on HVD metrics considering severe, moderate, and slight environmental changes respectively. Also, it is the winner for solving 8 DMOPs based on RIGD, and RGD metrics. •Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO).•Distributed MOPSO allows higher diversity during the search process.•Distributed sub-swarms conduct independent search with separate pools of best solutions.•The DPb-MOPSO system includes a dynamic handling strategy to manage the evolution of the search space.•The dynamic switch mechanism allows to recover from any engagement within a local optimum.